Background

Travel is an important activity that people in the United Kingdom (UK) participate in for leisure, business and visiting family. Choices are influenced by a number of personal and wider influences (such as the economic or political).

Individual travel choices can also have implications for the economy and environment. This makes understanding trends important. Additionally in the past 10 years the UK has seen a number of events which theoretically could have influenced these travel choices (e.g. Coronavirus pandemic, EU Exit).

Research question

Overall have people in the UK changed their chosen travel destinations over time?

Data Origins

The data was provided by the UK’s Office for National Statistics (ONS) and accessed in May 2023 at their Travel Trends Dataset Pages. The only available data was between 2009 and 2021. The specific files used were:

I have used the “Number of visits to specified countries: by main country visited and nationality” as this allowed a more nuanced view of travel than region.

Data collection

The data used in this project provides estimated visits based on the International Passenger Survey (IPS). The IPS uses interviews at all major airports, sea routes and Eurostar/ Eurotunnel terminals with approximately 250,000 people included in the estimates (although up to 800,000 interviews take place). There are no other details on the sampling method. Countries are consistent for every year available.

However the data for 2021 is more limited than previous years of the IPS due to a smaller sample. Reasons for this included the smaller number of travelers and issues collecting the data at key terminals (e.g. Eurotunnel). Additionally a new method for calculating the 2021 estimates was introduced. Full details of both the changes and data distruption are in the raw data files.

Overall the scale and coverage of the IPS make it reliable for our research question, bearing in mind caveats around 2021. After reviewing the overlapping years within the data sets the number of visits matched; making a merge still appropriate. Finally other data sources of a better or similar quality were not available.

Initial data

The initial data for this project was relatively clean and within a workbook of multiple statistics. The table below shows the first few rows of how the initial data for 2017 to 2021.

data1721 = (here("raw_data","section3ukresidentsvisitsabroad2017to2021.xlsx")) 
#data for 2017 to 2021
raw<- read_excel(data1721,
                    sheet="3.06", 
                    skip=10,
                    n_max=68)
## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...8`
head(raw)

Data definitions

A full codebook describing the variables from the original data set and any calculated values are available in the github pages for this project.

The primary variables taken from the initial data are in the table below.

variable description other_notes
country (1 in the raw data) main destination travelled to from the UK Source ONS raw data apart from the dfColour
2009…2021 (each years individual variable) number of visits in thousands (000s) This includes business, leasure and family travel. In dfv2 years correspond to ranks

Data preperation

This section covers the different processes that this data has been through prior to visualisation.

Libraries and RENV

The following packages were used in this project:

renv::restore()
#Libraries
library(here)#Here for loading in the data
library(tidyverse)#Tidyverse for data management
library(ggplot2) #chart making
library(readxl)#Reading in Excel sheets as that is how the data is stored 
library(plotly)#Making scroll over charts
library(htmlwidgets)#To save interactive chart
library(knitr) #To make a table for the RMarkdown

To support future replication this R Project used RENV (version:0.17.3) to keep packages consistent and details together. An additional document with package citations is available in the following file location.

Importing the data

Creating cleaning variables

As an initial step a set of values was created to support the importation and data cleaning processes. In theory these will allow for new data sets to be added in a simple way.

# 2. Load the data in

#Variables required for data cleaning of the ONS files

ONSList <- 68 #The length of the ONS data table, these are consistent

yearsdf1721 <- c("2017","2018","2019",
                 "2020","2021") # These are the years that we will want to take from this data frame

yearsdf0919 <- c("2009","2010","2011",
                 "2012","2013","2014",
                 "2015","2016") #Years wanted, always choose most recent

summaries <- list('Total World',
                  'Other Countries',"Europe",
                  '- of which EU',
                  '- of which EU Oth',
                  '- of which EU15',
                  "North America") #summary values used in the dataset

# Raw data locations
data1721 = (here("raw_data","section3ukresidentsvisitsabroad2017to2021.xlsx")) #data for 2017 to 2021
data0919 = (here ("raw_data","section3ukresidentsvisitsabroad2009to2019.xlsx")) #data for 2009 to 2017

Importing the data

The following code imports the data into R. It then renames the columns to match those in the original ONS data file and removes the rows that are blank.

# Load in latest dataset 2017-2021            
df1721<- read_excel(data1721,
                    sheet="3.06", 
                    skip=10,
                    n_max=ONSList)

##rename columns 2017-2021
df1721_n <- tibble(x = 1:9, y = c("country", "coltoremove", "2017","2018",
                                  "2019","2020","2021","blankroremove",
                                  "avgrowth1519"))#list of column names

names(df1721) <- df1721_n %>% select(y) %>% pull()#function to change the names 

##Remove NAs in rows and select years only 2017 to 2021
df1721 <- df1721 %>% 
          select("country",all_of(yearsdf1721)) %>% #Select correct years
          drop_na("country")# remove blank rows

## Read the excel file for 2009 to 2019
df0919 <- read_excel(data0919,
                     sheet="3.10", 
                     skip=10,
                     n_max=ONSList)

###rename columns 2009 - 2019
df0919_n <- tibble(x=1:19, y= c("country", 
                                "coltoremove",
                                "2009","2010","2011",
                                "2012","2013","2014",
                                "2015","2016","2017",
                                "2018","2019",
                                "blank1",
                                "change1819",
                                "blank2",
                                "growth1819",
                                "blank3",
                                "Avgrowth1519"))#list of column names

names(df0919) <- df0919_n %>% select(y) %>% pull() #function to change the names 

###Remove NAs in rows and select years only to 2017
df0919 <- df0919 %>% 
          select("country",all_of(yearsdf0919)) %>% #Select correct years
          drop_na("country")# remove blank rows

Merge the files

Once both data sets were in the same shape they were them into a single data frame that has all available years. This was then when I removed the summary variables to avoid uneccessary code.

#Merge the datasets
df0921 <- left_join(df0919, df1721,by="country")

##Remove summaries in the dataset
df0921 <-df0921 %>% 
         filter((!country %in% summaries))

Use of ranks

As 58 countries is tricky to visualise I knew a rank variable would be required to develop the visualisation. I decided on 2021 to be the key year for this cut off because it is the most recent and presents a “now” vs the “past” view which is more intuitive. The ranks work on 1 being the most visited country.

## Add ranking variable for 2021 (this will be used to decide on the cases kept)
df0921 <- df0921 %>% 
           mutate(rank_cut = rank(-`2021`, ties.method = "average"))

#save combined data set
comData_n = paste(here("created_data"), "/travel0921.csv", sep = "")   
write.csv(df0921, comData_n)   #Write data to CSV
#check data
head(df0921)

Visualising the data

I decided that there were three approaches to visualising the data:

  1. Use the raw visits data.
  2. Report the proportion of visits to each country.
  3. Use the country’s rank score.

Visits and ranks were seen as the most promising for visualisations as proportions would require more of the countries to be included in the plot. It was also assumed that an audience would be more interested in the most visited rather than proportions.

Other considerations:

Visualisation 1 - Dumbell plot

With visualisation 1 I wanted to use the raw number of visits to show the change in travel. I decided on a dumbell plot because I felt that it showed the differences between both 2009 and 2021 and the countries well. It is also less cluttered than a traditional clustered bar chart and allowed for easier comparisons between countries.

Data changes

Initially I created variables to select the years and the number of countries I wanted included. This was to allow for any quick changes based on how the final graph was looking. After this the data was then moved into a long format with the year column name becoming the year variable. The number of visits for each year is included as visits.

#3.1. Create variables to allow us to choose the number of 2021 ranks 

cutrank1 <- 20 #number of countries that we want included in the chart
yrs_inc1 <- c(2009, 2021) # years that we want to include (can be any year from 2009-2021 apart from 2020)

#3.2 Create summary DF based on the number of countries

dfv1 <- df0921 %>% 
        filter (rank_cut<=cutrank1)

#3.3. Convert to long format

longdfv1 <- dfv1 %>% select("country", 
                            "2009", "2010", 
                            "2011", "2012", 
                            "2013", "2014", 
                            "2015", "2016", 
                            "2017", "2018",
                            "2019", "2021") %>% #2020 dropped due to no data 
                      pivot_longer(!country, 
                                   names_to = "year", 
                                   names_transform = list(year = as.factor), #Changed to factor to allow for it to be treated as a category
                                   values_to = "visits") %>%
                      arrange(desc(visits)) %>%  #order by the most visited to the least
                      filter(year %in% yrs_inc1) #removes years that we are not interested in as defined above

head(longdfv1)

Visualisation

To create the dumbell plot I added the visits data to geom_point and then added a line to connect the 2 dates together. As some data points overlap I ensured that these points were opaque. Additionally I created a dynamic subtitle in case the years chosen changes in the future (e.g. if we decide that the 2019 pre-covid year is a more appropriate comparison). I kept the colours as default as I felt that they actually worked quite well. The countries were reordered by the largest visits but this had mixed results.

#3.4. Create a dumbbell plot 

#3.4.1. Create a subtitle that changes with the data selected. 
subtitle1 <- paste("Showing the top", toString(cutrank1), "most visited countries in 2021")

#3.4.2. Match the aesthetics (basic plot)

p1 <- ggplot(longdfv1, 
            aes(x = visits, y = reorder(country, visits)))

#3.4.3. Add layers 
v1 <- p1 +   #lines to show the years are connected 
             geom_line(color="grey") + 
             #shows the year
             geom_point(aes(color = year), size = 3, alpha=0.8) +  
             #Legend at the bottom for ease
             theme(legend.position = "bottom") + 
             #Labels for the chart
             labs(x = "Number of visits from the UK (000s)", 
                  y = "Country", 
                  title = "Number of visits abroad from the UK in 2009 and 2021 by main country visited",
                  subtitle = subtitle1,
                  caption= "Source: Office for National Statistics International Passenger Survey") 
#3.5. Save the dumbbell plot 
ggsave(here("plots", "dumbell.png"), v1, width = 11.2, height = 7.163)

Interactive plot

As I felt that this plot made reading the actual number difficult I entered it into ggplotly to allow for scrolling over points. This was useful but did make some of the formatting less clear.

#3.6. Add the dumbbell to plotly to make it interactive

ip1 <- ggplotly(v1) #Use plotly for scrollover
#Display interactive visualisation 1
ip1
#3.7. Save the interactive plot
saveWidget(ip1, file='interactivedumbell.html')
#Image for PDF markdown (add if required)
#screen <- here("documents", "printscreen_ip1.png") #location of file 
#knitr::include_graphics(screen)

Interpretation - has there been a change in travel patterns?

From this visualisation we can see that most countries have seen a reduction in the number of visits from 2009 to 2021. This is especially true for Spain and France but they had a much higher number of visitors in 2009. Only Romania has a clear increase in visits. Although this is useful I don’t think it shows the changes in travel patterns in an intuitive way. Additionally it mostly confirms that travel during the end of the COVID-19 pandemic was lower which is not particularly insightful.

Visualisation 2 (final) - Slope chart

After reviewing plot 1 I decided that ranks would show the same information in a simpler way and provide a way to understand trends despite the impact of COVID-19 on traveler numbers. The trade off was losing the scale of difference between Spain and the rest of the countries.

I decided on a slope chart as this provides the most information without it being too cluttered. I was also conscious that other alternatives such as a line graph or paired bar chart would be difficult to interpret.

Data changes

As with visualisation 1 I made variables to allow for easy customisation of the number of countries/ years. Prior to visualising I needed to convert the data from actual visits to the rank position for that year. Following on from that I needed to select the number of countries that I wanted included and then convert to the long format. Finally I selected only cases in 2009 and 2021.

#4.1. Create variable to allow for filtering the data

cutrank2 <- 15 # Create variables to allow us to choose the number of 2021 ranks 
yrs_inc2 <- c(2009, 2021) #Create variables for the years to include in visualisation


#4.2. Create new DF of just ranks 

dfv2 <- df0921 %>% 
        mutate(r09 = rank(-`2009`, ties.method = "average"),
               r10 = rank(-`2010`, ties.method = "average"),
               r11 = rank(-`2011`, ties.method = "average"),
               r12 = rank(-`2012`, ties.method = "average"),
               r13 = rank(-`2013`, ties.method = "average"),
               r14 = rank(-`2014`, ties.method = "average"),
               r15 = rank(-`2015`, ties.method = "average"),
               r16 = rank(-`2016`, ties.method = "average"),
               r17 = rank(-`2017`, ties.method = "average"),
               r18 = rank(-`2018`, ties.method = "average"),
               r19 = rank(-`2019`, ties.method = "average"),
               r20 = r19, #no data so saying that it would have been steady from 19
               r21 = rank(-`2021`, ties.method = "average")) %>% #r09-#r21 are the rank scores for each year
        select("country", 
               "r09", "r10", "r11", 
               "r12", "r13", "r14", 
               "r15", "r16", "r17", 
               "r18", "r19", "r21",
               'rank_cut') %>% #selecting the ranked data but 2020 dropped due to no data
        rename('2009'='r09',
               '2010'='r10',
               '2011'='r11',
               '2012'='r12',
               '2013'='r13',
               '2014'='r14',
               '2015'='r15',
               '2016'='r16',
               '2017'='r17',
               '2018'='r18',
               '2019'='r19',
               '2021'='r21',
               'rank_cut2'='rank_cut') #changing rank names to years to allow for changing to long format

#4.3. Create summary DF based on the number of countries

dfv2 <- dfv2 %>% 
        filter (rank_cut2 <=cutrank2)

#4.4. Change to Long format        

longdfv2 <- dfv2 %>% 
            select("country", 
                   "2009", "2010", 
                   "2011", "2012", 
                   "2013", "2014", 
                   "2015", "2016", 
                   "2017", "2018",
                   "2019", "2021") %>%
            pivot_longer(!country, 
                          names_to = "year", 
                          names_transform = list(year = as.factor), #Changed to factor to allow for it to be treated as a category
                          values_to = "rank") %>%
            filter(year %in% yrs_inc2) #remove years that are irrelevant

head(longdfv2)

Colour scheme

An additional factor in the choice of country is the geographic region that it is in. To account for this I created a colour scheme where each country grouping (based on the ONS National statistics country classification). To develop this scheme each grouping got their own main colour with each individual country showing a different shade. The table below shows the data format.

#4.5.1. Retrieve data file containing the colours for each country/ continent
dataColour <- here("created_data", "colours.csv") #location of file 
dfColour <- read.csv(file = dataColour) # read in data

head(dfColour)

This was then combined with the long rank data.

#4.5.2. Match the colour file to the data by country
longdfv2 <- left_join(longdfv2, dfColour,by="country")

head(longdfv2)

Visualisation

To create the final visualisation I used a line chart added to points at the end of each line to highlight the colour and position changes. To support understanding which countries are in which order I added the country name and rank next to the chart.

Other details:

  • Subtitle and source that ammends automatically;
  • Slight transparency to see where the lines are changing;
  • Ordered 1 to lowest.
#4.6. Create the final visualisation (slope plot)

#4.6.1. Create titles for the plot

subtitle2 <- paste("Changes in the rank for overall visits (business and travel) for the top", 
                   toString(cutrank2),
                   " most visited countries from the UK between"
                   ,toString(first(yrs_inc2)), 
                   "to" ,
                   toString(last(yrs_inc2))) # Label explaining the data 

Source2 <- bquote(paste(~bold('Source:'),"ONS International Passenger Survey",
                        ~bold('Note:'),"Colours indicate country grouping; 2021 had disrupted data collection")) #data source and interpretation notes 
  
title2 <- paste("Viva Espania! Spain remains the UK's most visited country")

#4.6.2. Match the aesthetics (basic plot)

p2 <- longdfv2 %>% 
      ggplot(aes(x=year, y=rank,group=country, colour= country))


#4.6.3. Add the formatting  

final <- p2+
         #slope lines
         geom_line(alpha = 0.5, linewidth=1.5) + 
         
         #End points for the data 
         geom_point(size = 3.5) + 
         
         #Labels to show the country and rank next to each of the countries 
         geom_text(data= longdfv2 %>% filter(year==(toString(first(yrs_inc2)))), #selects the first year
                   aes(x=year, y=rank,label = country), #add the country name 
                   size = 4, nudge_x = -0.35, fontface = "bold", color = "#000000") + # format so next to text
  
         geom_text(data= longdfv2 %>% filter(year==(toString(last(yrs_inc2)))), #selects the last year 
                   aes(x=year, y=rank,label = country), # add the country name
                   size = 4, nudge_x = 0.35, fontface = "bold", color = "#000000") + #format so next to text 
  
         geom_text(data= longdfv2 %>% filter(year ==(toString(first(yrs_inc2)))), #select the first year
                   aes(x=year, y=rank,label = rank), #add the rank position
                   size = 4, nudge_x = -0.05, fontface = "bold", color = "#000000") + #place next to country name
  
         geom_text(data= longdfv2 %>% filter(year ==(toString(last(yrs_inc2)))), #select the last year
                   aes(x=year, y=rank,label = rank), # add the rank to the chart
                   size = 4, nudge_x = 0.05,fontface = "bold", color = "#000000") + #formatting 
         
         #Reverse the scale so that the lowest ranked (most visited) is at the top
         scale_y_reverse( breaks=NULL) +
         
         #Use custom colours 
         scale_color_manual(values = setNames(longdfv2$hex_value, longdfv2$country)) +
  
         #Add labels
         labs(x = "Year", #X-axis
              y = "Rank visits from the UK (1 is the most visited)", #y-axis
              title = title2, #main title
              subtitle = str_wrap(subtitle2, width=150), #subtitle 
              caption= Source2) + #caption with each on a new line
         
         #Specify the theme (fonts, background etc.)
         theme(plot.title = element_text(size=18, face = "bold"), #Main title font
         axis.text.x = element_text(size=14, face="bold", colour="black"), #X-axis element font
         axis.text.y = element_text(size=12, face="bold"), #y-axis years font
         axis.title = element_text(size=14,face = "bold"), #Axis title fonts 
         panel.background = element_blank(), #Make the background blank
         legend.position = "none",#Remove the legend 
         plot.caption = element_text(size = 10, hjust = 1)) #Caption font 
#4.6.4. Save the final plot
ggsave(here("plots", "finalplot.png"), final, width = 13.44, height = 8.595)

Interpretation - has there been a change in travel patterns?

The slope chart of ranked data is much clearer to interpret because the steepness of the slopes shows the trend. Overall there has been a slight change in travel patterns but this is not drastic or shifting the areas of the World that people are visiting.

However more specific insights include:

  • Spain and France have remained first and second in the ranks.
  • Since 2009 only 3 countries have entered/ left the top 15 ranked countries.
  • Romania has had the most dramatic increase in rank from 43 to 9 followed by other Middle East.
  • The USA saw the greatest drop in rank position and remained in the top 15.
  • Most countries are in Europe as indicated by the blue colours with only 3 countries in other global regions.

Reflections

The main reflection that I have on the visualisations is the difficulties in trying to provide all the information available in a simple format. Although it can seem inappropriate to lose data on actual visits and the scale of the Spanish visits this was necessary to see the big picture.

With visualisation 1 I think that the chart type and overall look is good. However the difficulties managing the scale and getting the countries to order as planned made it not fully effective.

I think that visualisation 2 is sufficient to get an overall idea of the trends with UK travel. I like the colour scheme and that it looks simple whilst containing the necessary information. Likewise the subtitles that automatically change were another strength.

The final visualisation does have some limitations:

If there was more time and access to the full data set for the IPS since 1961 I would have liked to create an interactive dashboard of the ranked travel data. This would allow people to type in their countries of interest and see longer term trends. This could also allow people who are interested to click on the position to see the actual number of visits. An example of this is provided by the ONS on 100 years of baby names.

Overall have people in the UK changed their chosen travel destinations over time?

From the two visualisations the travel choices in the UK apear to have remained relatively consistent in the UK. However there do appear to be some changes that would hopefully become more clear in the future.

References

Additionally all the different free R resources online and stackoverflow have been useful in producing these charts.